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normalized_columns_initializer

Syntologyentry name in harvested coderead from the graph 2026-09-24

normalized_columns_initializer appears in the code Syntology harvested for 8 papers, as 5 distinct code bodies found in 9 places (a place is one code body under one paper). At least one of them ran in 7 of the papers; 2 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named normalized_columns_initializer do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 4 of the 5 distinct code bodies named normalized_columns_initializer; 1 is unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
3ran · our draft was wrong
0ran · fixture could not drive it
1ran
1unverified
2fingerprinted

Licence is a property of each copy, so it is counted per place: 3 of the 9 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

8 papers shown of 8, newest first; 9 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding 23 Feb 2024 kaist-silab/himap/src/models/vggnet.py 157c2af2dc5c2860 ran no licence file found · pointer only
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning 16 Mar 2020 shiwj16/SSINet/src/networks.py 96708b203eb62ef3 ran · our draft was wrong fingerprinted MIT (permissive)
Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in Video 18 Jan 2020 WuJie1010/TSP-PRL/model.py e0cbd595581568d9 ran · our draft was wrong fingerprinted no licence file found · pointer only
Attentive Multi-Task Deep Reinforcement Learning 5 Jul 2019 braemt/attentive-multi-task-deep-reinforcement-learning/model.py ef2d50dfca29c664 ran · our draft was wrong MIT (permissive)
Curiosity-driven Exploration by Self-supervised Prediction 15 May 2017 pathak22/noreward-rl/src/model.py ef2d50dfca29c664 ran · our draft was wrong licence not identified · pointer only
Evolution Strategies as a Scalable Alternative to Reinforcement Learning 10 Mar 2017 atgambardella/pytorch-es/model.py 2b7f271057272097 unverified MIT (permissive)
Learning to reinforcement learn 17 Nov 2016 awjuliani/Meta-RL/helper.py ef2d50dfca29c664 ran · our draft was wrong MIT (permissive)
Asynchronous Methods for Deep Reinforcement Learning 4 Feb 2016 dsinghnegi/atari_RL_agent/models/a3c.py 96708b203eb62ef3 ran · our draft was wrong fingerprinted Apache-2.0 (permissive)
Asynchronous Methods for Deep Reinforcement Learning 4 Feb 2016 openai/universe-starter-agent/model.py ef2d50dfca29c664 ran · our draft was wrong MIT (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections